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FalkorDBQAChain | Документация для разработчиков

FalkorDBQAChain

Обновлено 6 марта 2024

This notebook shows how to use LLMs to provide a natural language interface to FalkorDB database.

FalkorDB is a low latency property graph database management system. You can simply run its docker locally:

docker run -p 6379:6379 -it --rm falkordb/falkordb:edge

Once launched, you can simply start creating a database on the local machine and connect to it.

from langchain.chains import FalkorDBQAChain
from langchain_community.graphs import FalkorDBGraph
from langchain_openai import ChatOpenAI

Create a graph connection and insert some demo data.

graph = FalkorDBGraph(database="movies")
graph.query(
"""
CREATE
(al:Person {name: 'Al Pacino', birthDate: '1940-04-25'}),
(robert:Person {name: 'Robert De Niro', birthDate: '1943-08-17'}),
(tom:Person {name: 'Tom Cruise', birthDate: '1962-07-3'}),
(val:Person {name: 'Val Kilmer', birthDate: '1959-12-31'}),
(anthony:Person {name: 'Anthony Edwards', birthDate: '1962-7-19'}),
(meg:Person {name: 'Meg Ryan', birthDate: '1961-11-19'}),

(god1:Movie {title: 'The Godfather'}),
(god2:Movie {title: 'The Godfather: Part II'}),
(god3:Movie {title: 'The Godfather Coda: The Death of Michael Corleone'}),
(top:Movie {title: 'Top Gun'}),

(al)-[:ACTED_IN]->(god1),
(al)-[:ACTED_IN]->(god2),
(al)-[:ACTED_IN]->(god3),
(robert)-[:ACTED_IN]->(god2),
(tom)-[:ACTED_IN]->(top),
(val)-[:ACTED_IN]->(top),
(anthony)-[:ACTED_IN]->(top),
(meg)-[:ACTED_IN]->(top)
"""
)
    []

Creating FalkorDBQAChain

graph.refresh_schema()
print(graph.schema)

import os

os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
    Node properties: [[OrderedDict([('label', None), ('properties', ['name', 'birthDate', 'title'])])]]
Relationships properties: [[OrderedDict([('type', None), ('properties', [])])]]
Relationships: [['(:Person)-[:ACTED_IN]->(:Movie)']]

chain = FalkorDBQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)

Querying the graph

chain.run("Who played in Top Gun?")
    

> Entering new FalkorDBQAChain chain...
Generated Cypher:
MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE m.title = 'Top Gun'
RETURN p.name
Full Context:
[['Tom Cruise'], ['Val Kilmer'], ['Anthony Edwards'], ['Meg Ryan'], ['Tom Cruise'], ['Val Kilmer'], ['Anthony Edwards'], ['Meg Ryan']]

> Finished chain.
    'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'
chain.run("Who is the oldest actor who played in The Godfather: Part II?")
    

> Entering new FalkorDBQAChain chain...
Generated Cypher:
MATCH (p:Person)-[r:ACTED_IN]->(m:Movie)
WHERE m.title = 'The Godfather: Part II'
RETURN p.name
ORDER BY p.birthDate ASC
LIMIT 1
Full Context:
[['Al Pacino']]

> Finished chain.
    'The oldest actor who played in The Godfather: Part II is Al Pacino.'
chain.run("Robert De Niro played in which movies?")
    

> Entering new FalkorDBQAChain chain...
Generated Cypher:
MATCH (p:Person {name: 'Robert De Niro'})-[:ACTED_IN]->(m:Movie)
RETURN m.title
Full Context:
[['The Godfather: Part II'], ['The Godfather: Part II']]

> Finished chain.
    'Robert De Niro played in "The Godfather: Part II".'
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